Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and RBFNSM for wind turbine in the grid connected mode

doi:10.1007/s11708-017-0446-x

Front. Energy

2019, Vol. 13

Issue (1) : 131-148 https://doi.org/10.1007/s11708-017-0446-x

RESEARCH ARTICLE

Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and RBFNSM for wind turbine in the grid connected mode

Alireza REZVANI¹(

), Ali ESMAEILY², Hasan ETAATI³, Mohammad MOHAMMADINODOUSHAN⁴

¹. Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh 3919715179, Iran Water and Power Resources Development Company (IWPCO), Iran
². Department of Electrical Engineering, Karaj Branch, Islamic Azad University, Karaj 3148635731, Iran
³. Iran Water and Power Resources Development Company (IWPCO), Iran
⁴. Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran

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Abstract

Photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is intermittent because of depending on weather conditions. Therefore, the wind power can be considered to assist for a stable and reliable output from the PV generation system for loads and improve the dynamic performance of the whole generation system in the grid connected mode. In this paper, a novel topology of an intelligent hybrid generation system with PV and wind turbine is presented. In order to capture the maximum power, a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. The average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison with the conventional methods. The pitch angle of the wind turbine is controlled by radial basis function network-sliding mode (RBFNSM). Different conditions are represented in simulation results that compare the real power values with those of the presented methods. The obtained results verify the effectiveness and superiority of the proposed method which has the advantages of robustness, fast response and good performance. Detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink.

Keywords photovoltaic wind turbine hybrid system fuzzy logic controller genetic algorithm RBFNSM

Corresponding Author(s): Alireza REZVANI

Online First Date: 24 February 2017 Issue Date: 20 March 2019

Cite this article:

Alireza REZVANI,Ali ESMAEILY,Hasan ETAATI, et al. Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and RBFNSM for wind turbine in the grid connected mode[J]. Front. Energy, 2019, 13(1): 131-148.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-017-0446-x
https://academic.hep.com.cn/fie/EN/Y2019/V13/I1/131

Fig.1 Equivalent circuit of one PV array

Tab.1 Red sun 90 W module under STC

Tab.2 Genetic algorithm parameters

Fig.2 Diagram of the discussed method

Fig.3 Structure of fuzzy-neural hybrid method

Fig.4 The membership function of fuzzy logic

Tab.3 Fuzzy rules

Fig.5

Fig.6

Fig.7 Training data of ANN controller

Fig.8 Validation data of ANN controller

Fig.9 Testing data of ANN controller

Fig.10 Simulated results of variations of irradiance) in case 1

Tab.4 Tracking efficiency and response time comparison for different MPPT techniques under irradiance variation

Tab.5 Output power values of solar array (watt) in various irradiation conditions

Fig.11 Simulated results for wind system in case1

Fig.12 Simulated results of variations of irradiance) in case 2

Tab.6 Tracking efficiency and response time comparison for different MPPT techniques under temperature variation

Tab.7 Output power values of solar array (watt) in various temperature conditions

Fig.13 Simulated results for wind system in case 2

Tab.8 Performance comparison of RBFNSM and PI Controller

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